The present invention relates to computer modeling and simulation, more particularly to computer modeling and simulation representative of human decision-making.
It has been a long-standing challenge in warfare mission modeling to represent the decision-making ability of commanders in the field. Warfare commanders are confronted with continuous streams of information, and they act on their understanding of information in light of their objectives, capabilities and strategy. A key facet of the tactical decision making process of commanders is dealing with the myriad timelines, resources and opportunities in warfare.
Various quantitative decision-making algorithms (e.g., match filters, Kalmann filters, etc.) are known that seek to determine the optimal assignments of target classifications. Nevertheless, human decision-makers do not always make optimal decisions. Conventional approaches to representing human decision-making often use weighted factor trees, Bayesian networks, or “if-then-else” logic rules; however, these methods have high data requirements (experimental or subject matter expert), and generally require the modeler to fully articulate a wide range of possible conditions.
Furthermore, most human decision-making algorithms cannot represent decision-maker biases, either static or evolving. In addition, most conventional human decision-making algorithms cannot represent urgency at any given decision point or towards any particular decision option.